Defining the Contribution of AMP-activated Protein Kinase (AMPK) and Protein Kinase C (PKC) in Regulation of Glucose Uptake by Metformin in Skeletal Muscle Cells
Bibliographic record
Abstract
The importance of AMP-activated protein kinase (AMPK) and protein kinase C (PKC) as effectors of metformin (Met) action on glucose uptake (GU) in skeletal muscle cells was investigated. GU in L6 myotubes was stimulated 2-fold following 16 h of Met treatment and acutely enhanced by insulin in an additive fashion. Insulin-stimulated GU was sensitive to PI3K inhibition, whereas that induced by Met was not. Met and its related biguanide, phenformin, stimulated AMPK activation/phosphorylation to a level comparable with that induced by the AMPK activator, 5-amino-1-β-d-ribofuranosyl-imidazole-4-carboxamide (AICAR). However, the increase in GU elicited by AICAR was significantly lower than that induced by either biguanide. Expression of a constitutively active AMPK mimicked the effects of AICAR on GU, whereas a dominant interfering AMPK or shRNA silencing of AMPK prevented AICAR-stimulated GU and Met-induced AMPK signaling but only repressed biguanide-stimulated GU by ∼20%. Consistent with this, analysis of GU in muscle cells from α1(-/-)/α2(-/-) AMPK-deficient mice revealed a significant retention of Met-stimulated GU, being reduced by ∼35% compared with that of wild type cells. Atypical PKCs (aPKCs) have been implicated in Met-stimulated GU, and in line with this, Met and phenformin induced activation/phosphorylation of aPKC in L6 myotubes. However, although cellular depletion of aPKC (>90%) led to loss in biguanide-induced aPKC phosphorylation, it had no effect on Met-stimulated GU, whereas inhibitors targeting novel/conventional PKCs caused a significant reduction in biguanide-induced GU. Our findings indicate that although Met activates AMPK, a significant component of Met-stimulated GU in muscle cells is mediated via an AMPK-independent mechanism that involves novel/conventional PKCs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".